/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing,
 * software distributed under the License is distributed on an
 * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
 * KIND, either express or implied.  See the License for the
 * specific language governing permissions and limitations
 * under the License.
 */
package org.apache.iotdb.library.dprofile.util;

import org.apache.commons.math3.linear.*;

import java.util.Arrays;

/**
 * Describe class here.
 */
public class YuleWalker {
    public double yuleWalker(double[] x, int order, String method, int n) {
        double adj_needed = method.equalsIgnoreCase("adjusted") ? 1 : 0;
        double[] r = new double[order + 1];
        double squaresumx = 0d;
        for (double v : x) {
            squaresumx += v * v;
        }
        r[0] = squaresumx / n;
        for (int k = 1; k < order + 1; k++) {
            double[] t1 = Arrays.copyOfRange(x, 0, n - k);
            double[] t2 = Arrays.copyOfRange(x, k, n);
            double crossmultiplysum = 0d;
            for (int i = 0; i < Math.min(t1.length, t2.length); i++) {
                crossmultiplysum += t1[i] * t2[i];
            }
            r[k] = crossmultiplysum / (n - k * adj_needed);
        }
        // R is a toeplitz matrix
        double[][] R = new double[r.length - 1][r.length - 1];
        for (int i = 0; i < r.length - 1; i++) {
            for (int j = 0; j < r.length - 1; j++) {
                R[i][j] = r[Math.abs(i - j)];
            }
        }
        RealMatrix a = new Array2DRowRealMatrix(R, true);
        RealVector b = new ArrayRealVector(Arrays.copyOfRange(r, 1, r.length), true);
        DecompositionSolver solver = new LUDecomposition(a).getSolver();
        try {
            RealVector rho = solver.solve(b);
      /*
      sigmasq = r[0] - (r[1:]*rho).sum()
      sigma = np.sqrt(sigmasq) if not np.isnan(sigmasq) and sigmasq > 0 else np.nan
      */
            return rho.getEntry(rho.getDimension() - 1);
        } catch (SingularMatrixException e) {
            return Double.NaN;
        }
    }
}
